Assistive mechanism via edge device personalization

ABSTRACT

A method, system, and computer program product provide navigation guidance around various object types in a vicinity of a guidance assistance device for a user by analyzing received data regarding an environment around the guidance assistance device to identify one or more entities Ei and by applying an artificial intelligence machine learning analysis to group the one or more entities Ei into corresponding categories Cj and to determine a minimum spacing distance Dmin for each of the one or more entities Ei, wherein the minimum spacing distance Dmin is a minimum distance between the guidance assistance device and the entity Ei based on the categorization Ci specified by the user, and then providing feedback to the user when any of the one or more entities Ei is within than minimum spacing distance Dmin corresponding to said entity.

BACKGROUND OF THE INVENTION

With the burgeoning demand for assistive devices to aid people sufferingfrom visual or aural impairment, there are guidance devices orrelationships to assist them in navigating and interacting with theirenvironments. For example, some blind people may use a cane or guideanimal in order to navigate and interact with an environment, but thereare also applications and embedded hardware controls which help withnavigation through physical and/or virtual surroundings. However, thesedevices are of limited use in terms of assisting thesensorially-impaired person as they tend to possess static andunchanging designs and/or operational performance which are designed fora generic user and/or unspecified operating environment. For example, agenerically designed guidance device may not be able to detect specificconditions or navigation obstacles in a given environment may pose arisk that is unique to a particular person. As a result, the existingsolutions for guiding sensory impaired people navigating and interactingwith their environments are deficient at a practical and/or operationallevel.

SUMMARY

Broadly speaking, selected embodiments of the present disclosure providea guidance assistance device, system, method, and apparatus foroptimally aiding the navigation of a visually-impaired user through anenvironment by processing video/image data of an environment collectedby a camera to dynamically identify and classify different objects orentities Ei into associated categorizations Ci (C1, C2, . . . , Cn) anda detected distances Di (D1, D2, . . . , Dn) for purposes of evaluationagainst user-specific distance preferences for each category by applyingartificial intelligence (AI) machine learning (ML) techniques, such as areinforcement learning model, to provide feedback information to theuser about the location or position of selected objects or entities inthe environment. In selected embodiments, the guidance assistance deviceis embodied as an edge device that is personalized for avisually-impaired user to assist with optimal navigation through anenvironment by dynamically classifying different objects/entities andcorrelating the edge device to respond to the different objects/entitiesin a personalized way for the given user via reinforcement learningmodel and synchronization with other edge devices to provide assistivefeedback to the user. The guidance assistance device may providepersonalized feedback information based on user feedback and/or userheuristics which specify proximity distance measures for differentobjects/entities, such as people (male or female individuals), animals(e.g., menacing or friendly dogs or cats), moving or stationaryvehicles, etc. For instance, a given user may specify a minimum distanceto keep from people owning pets or even high-speed vehicles driving onthe curb side (user's preferences may vary based on theirnature/demographics), but may specify a different minimum distance wherethe user is more comfortable with other physical entities, like kidswalking around the park/pavement. In addition, the minimum distances maybe dynamically adjusted based on a state of the user (e.g., the user'scomfort level) and/or the identified object/entity (e.g., a minimumdistance is increased for a growling or barking dog). In selectedembodiments, the guidance assistance device disclosed herein may providehaptic/auditory feedback to the user to keep a minimum specifieddistance from an identified object along a line of sight by using amachine learning model which adapts to user input specifying theadversity/repercussions of getting too close to objects which the userconsiders to be dangerous or undesirable. For example, the feedbackinformation may be provided to a wearable device which indicates to theuser when an object or entity is within a minimum specified distance tothe user with feedback selected from a group consisting of audio,visual, and haptic based on user's profile. In this way, each guidanceassistance edge device may be trained to work in sync with other edgedevices based on identifying or determining the external entities andingesting personalized heuristics to optimally aid the visually impaireduser in navigating through a given contextual environment in an adaptivefashion. In addition to being used with visually impaired users, theguidance assistance device may be used to assist autistic consumers whohave different preferences in navigating through crowded spaces.

The foregoing is a summary and thus contains, by necessity,simplifications, generalizations, and omissions of detail; consequently,those skilled in the art will appreciate that the summary isillustrative only and is not intended to be in any way limiting. Otheraspects, inventive features, and advantages of the present invention, asdefined solely by the claims, will become apparent in the non-limitingdetailed description set forth below.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention may be better understood, and its numerousobjects, features, and advantages made apparent to those skilled in theart by referencing the accompanying drawings, wherein:

FIG. 1 is a simplified illustration showing a user navigating an exampleenvironment using a guidance assistance device in accordance withselected embodiments of the present disclosure.

FIG. 2 depicts a system diagram that includes a guidance assistancedevice connected in a network environment to a computing system thatuses a reinforcement learning model to provide personalized navigationassistance in accordance with selected embodiments of the presentdisclosure;

FIG. 3 is a block diagram of a processor and components of aninformation handling system such as those shown in FIG. 2 ; and

FIG. 4 illustrates a simplified flow chart showing the logic for aidingthe navigation of a user through an environment in accordance withselected embodiments of the present disclosure.

DETAILED DESCRIPTION

The present invention may be a system, a method, and/or a computerprogram product. In addition, selected aspects of the present inventionmay take the form of an entirely hardware embodiment, an entirelysoftware embodiment (including firmware, resident software, micro-code,etc.) or an embodiment combining software and/or hardware aspects thatmay all generally be referred to herein as a “circuit,” “module” or“system.” Furthermore, aspects of the present invention may take theform of computer program product embodied in a computer readable storagemedium (or media) having computer readable program instructions thereonfor causing a processor to carry out aspects of the present invention.Thus embodied, the disclosed system, a method, and/or a computer programproduct is operative to improve the functionality and operation of acognitive question answering (QA) systems by efficiently providing moreaccurate training QA pairs.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a dynamic or static random access memory(RAM), a read-only memory (ROM), an erasable programmable read-onlymemory (EPROM or Flash memory), a magnetic storage device, a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Java, Smalltalk, C++ or the like,and conventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server or cluster of servers. Inthe latter scenario, the remote computer may be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection may be made to anexternal computer (for example, through the Internet using an InternetService Provider). In some embodiments, electronic circuitry including,for example, programmable logic circuitry, field-programmable gatearrays (FPGA), or programmable logic arrays (PLA) may execute thecomputer readable program instructions by utilizing state information ofthe computer readable program instructions to personalize the electroniccircuitry, in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

Embodiments described herein may permit a sensory-impaired user toquickly and efficiently navigate or otherwise interact with the user'senvironment. Sensory-impaired users may use a guidance assistance devicewhich combines video processing and artificial intelligence techniquesto provide guidance to the user about the location or distance proximityto objects in the environment to aid the user's interaction therewith.The guidance assistance device may detect information about objects inthe environment, model the environment using a reinforcement learningmodel and specified user preferences, and present guidance output basedon the model with user feedback that may be tactile so the user canquickly and efficiently navigate the environment. In the disclosedguidance assistance devices for sensory impaired users, sensor data maybe obtained regarding an environment around a guidance device,assistance device, environmental exploration device, an/or other suchdevice, such as by using a camera that is embedded in an assistedreality (AR) device being worn by the user to communicate with user'smobile/wearable devices. A reinforcement learning model of theenvironment may be generated based on the sensor data and user-specifieddistance preference information to generate tactile output for guidingthe user through the environment by specifying minimum distance measuresto detected objects in the environment. The guidance assistance devicemay include a variety of different components, such as sensors thatobtain data regarding the environment, input/output mechanisms forreceiving input from and/or providing input to the user, processingunits and/or other components for generating the model and/or mappingthe model to various input/output mechanisms, and so on. Additionally,the guidance assistance device may synchronize, cooperate and/orcommunicate with a variety of different edge devices that have one ormore such components in order to perform one or more of these functions.These and other embodiments are discussed below with reference to FIGS.1-4 . However, those skilled in the art will readily appreciate that thedetailed description given herein with respect to these Figures is forexplanatory purposes only and should not be construed as limiting.

Turning now to FIG. 1 , there is shown a simplified illustration of auser 11 using a guidance assistance device 12-14, environmentalexploration device, and/or other such device to navigate an exampleenvironment 10 in which a number of objects 15-17 are located.Illustrated as a combination of networked devices 12-14, the guidanceassistance device components are connected and configured to detectinformation about the user's environment 10 and to present thatinformation to the user to aid the interaction of the user 11 with theenvironment 10. In the illustrated example, the user 11 is wearing apair of smart glasses 12 which include an embedded camera device and awireless transceiver. In addition, the user 11 may be wearing a wearabledevice, such as a watch 13 and/or mobile device 14 while walking down astreet. Along the street in front of the user 11 is a menacing dog 15off to one side, an adorable kitten 16 on the sidewalk, and anapproaching vehicle 17 on the road, and so on. With an embedded cameraon the smart glasses 12, visual information 15A about the dog 15 iscollected by the guidance assistance device 12-14, along with visualinformation 16A about the kitten 16 and visual information 17A about thevehicle 17. Using one or more processor devices at the watch 13 and/ormobile device 14, an object identification or classifier analysis may beapplied to the captured visual information 15A-17A to identify entitiesE (E1, E2, . . . En), followed by additional clustering analysis togroup the identified entities into categories C (C1, C2, . . . , Cn). Inaddition, the processor device(s) may compute or otherwise determine acorresponding distance measures D (D1, D2, . . . , Dn) to each entity.

At the guidance assistance device 12-14 (or a remote server computersystem), artificial intelligence and/or machine learning analysis isapplied to determine a minimum distance from the user to each identifiedentity Ei based on the categorization Ci preferred by a user. Forexample, a reinforcement learning model may be applied to respond in apersonalized form to the given user to determine the preferences of theuser 11 for distance proximity to the detected objects 15-17. As theguidance assistance device 12-14 continues to measure the distancebetween to each entity, the guidance assistance device 12-14 may beconfigured to provide the user 11 with a tactile, audio, and/or otherguidance feedback related to the environment 10 upon detecting that anobject located in the environment 10 is approaching or within a minimumdistance for the object. Though not expressly shown, the guidanceassistance device component(s) 12-14 may be wired and/or wirelesslytransmitting and/or receiving information with one another and/or with awireless network of devices (not shown) in order to communicate with oneor more of each other. Such devices may communicate with each other inorder to obtain environmental or other sensor data regarding theenvironment, generate a model based on the sensor and user preferencedata, and provide the guidance output to the user 11 as described morefully hereinbelow.

To provide additional details for an improved understanding of selectedembodiments of the present disclosure, reference is now made to FIG. 2which schematically depicts a system diagram 100 of one illustrativeembodiment of a guidance assistance device 20 which includes a visualinput device 1 and computing system 21 connected in a networkenvironment 30 to a server computing system 101 that uses areinforcement learning model 109 to provide personalized navigationassistance to a user. In the depicted example, the visual input device 1may be embodied as a pair smart glasses having a lens frame and templeor earpiece extensions. As depicted, a camera 3 may be embedded in thelens frame and/or temple piece of the visual input device 1 to capturevisual information about the surrounding environment. In addition, anassist processor 2, such as a single chip microcomputer (SCM), may beaffixed or embedded to the lens frame and/or temple piece to performdigital processing on the captured visual information and/or to providea communication link 6 over a wireless transceiver (not shown) to thecomputing system 21. The visual input device 1 may also include anoptical head-mounted display (OHMD) 4 or embedded wireless glasses withtransparent heads-up display (HUD) or augmented reality (AR) overlay 5.

Visual information captured by the visual input device 1 may beprocessed in whole or in part with the assist processor 2 and/or thecomputing system 21 which is illustrated as including one or moreprocessing devices 22 and one or more memory devices 23 which areoperatively connected together with other computing device elementsgenerally known in the art, including buses, storage devices,communication interfaces, and the like. For example, the processingdevice(s) 22 may be used to process the captured visual information data6 received from the visual input device 1 for storage in the memorydevice(s) 23 which stores data 24 and instructions 25. In accordancewith selected embodiments of the present disclosure, the stored data 24and instructions 25 may embody a visual assist engine 26 which isconfigured provide guidance assistance for optimally aiding thenavigation of a visually-impaired user through an environment byprocessing video/image data of an environment collected by the visualinput device 1.

To this end, the visual assist engine 26 may include a first classifiermodule 27A for dynamically identifying and classifying different objectsor entities Ei in the captured visual information as the user or agentis navigating through the environment. In selected embodiments, thefirst classifier module 27A may employ any suitable classification orobject detection algorithm, such as Region-Based Convolutional NeuralNetworks (R-CNN), fast R-CNN, faster R-CNN, or the like, to identify theobjects or entities in space while the user/agent is navigating througha given environment. For example, an R-CNN object detection module 27Amay be used to monitor the state space by segmenting the captured visualcontext/video into chunks of frames F={f1, f2, f3 . . . fn} with deltavariations in time T={t1, t2, t3 . . . tn}, and then identifyingdifferent entities E (E1, E2, . . . En) in the environmental space wherethe user/agent is navigating through said frames. In this way, thevisual assist engine 26 analyzes the captured visual information toidentify objects or entities in the proximate environment of theuser/agent.

To further assist with object/entity identification, the visual assistengine 26 may also include a second cluster module 27B for clusteringthe identified object/entities into specific categories (such as kids,dogs, cats, person (male/female), vehicle, vehicle type, etc.) forstorage in the data frame. In selected embodiments, the second clustermodule 27B may employ any suitable classification and regressionalgorithm, such as k-nearest neighbor (KNN), k-means clustering, or thelike, to group or cluster the identified objects/entities intocategories. For example, a K-nearest neighbor (KNN) algorithm may beused in conjunction with CNN to cluster the entities into a specificcategory by assessing the type of data frame and associated attributes.For example, if the identified entities from the capture video are kidsof varying ages but seemingly young, they would be clustered by the KNNmodule 27B into a first category Kl. In addition, entities identified asMale would be clustered by the KNN module 27B into a second category M,entities identified as Female would be clustered as a third category F,and entities identified as a Vehicle would be clustered into a fourthcategory V. In addition, each category may have a specified categoricalfeature set activated. For example, a Vehicle type can be clustered intodifferent categories (Large (truck), medium (sedan/cars), Small(motorbike/scooters), etc.

The visual assist engine 26 may also include a third distance measuremodule 27C for measuring or detecting the distance between theuser/agent and each identified object/entity. In selected embodiments,the third distance measure module 27C may employ any suitable distancemeasurement technique, including but not limited to analysis of thecaptured visual information, radar-based measurements, or the like. Forexample, the smart glasses 1 may compute the detected distances Di (D1,D2, . . . Dn) from the smart glasses to each entity Ei (E1, E2, . . .En) by using known parameters (e.g., knownWidth, focalLength, perWidth)to compute a return distance from the entity to the glass or tootherwise determine a current user/agent position p and distance d fromthe object/entity.

Either at the guidance assistance device 20 or a remote server computingdevice 101, a machine learning model is trained and deployed to allowthe user/agent to keep the appropriate distance from objects in thereal-world space based on reinforcement learning for defined categoriesof objects in the space (e.g., people, dogs, cats, heaters, cars, etc.),each of which has a defined hostility/personal space profile or minimumdistance requirement specifying how close an individual would prefer tobe in spatial relation to the identified entities or objects as theynavigate a path to their destination. For example, the computing system21 may include a machine learning (ML) model 28, such as a reinforcementlearning model, that is deployed to generate an optimal notification toassist the user in navigating through the given environment. Inaddition, each guidance assistance device 20 may be trained to work insync with other guidance assistance devices which are configured toidentify external entities and ingest personalized heuristics forassisting the user in navigating through a given contextual environmentin an adaptive fashion.

In selected embodiments, the machine learning (ML) or reinforcementlearning (RL) model 28 may be configured with control code and/orsoftware functionality for generating state transition probabilities,observation probabilities, a reward function, or any combination thereofbased on one or more modeled or observed events. In an exampleembodiment, the reinforcement learning model 28 may be trained toevaluate one or more events or interactions, which may be simulatedevents (such as identifying an object or entity in the proximateenvironment of the use, associate sounds with entities or theenvironment), and to generate or modify a corresponding model, or asolution thereof, in response to the event. For example, a user who istraversing a street intersection may use the reinforcement learningmodel 28 to indicate a user navigation control action for navigatingalong a street path by prescribing specific minimum distances for anyobject detected in the path. To this end, the visual assist engine 26may be configured to provide the RL model 28 with outputs from the firstclassifier (R-CNN) module 27A and second cluster (KNN) module 27A wherethey are processed as state-environment parameters in the reinforcementlearning model framework 28 which computes user/agent diversion actionsbased on ingestion of heuristics pertaining to classification ofdifferent objects in said frames in increasing level of risk. Forexample, the RL model 28 may compute a reward function Rf value based onthe user's risk or comfort score with respect to an identified object orentity, such that the reward function Rf is increased if the risk scoredecreases for the user/agent, and is decreased if the risk scoreincreases for the user/agent:

-   -   a. Rf=+1 if the risk score <0.5, and    -   b. Rf=−1 if the risk score >0.5

As a result, reinforcement learning model framework 28 may continuouslymonitor the relative proximity distances between the user/agent andidentified objects in order to detect when the user/agent moves tooclose to an object deemed as dangerous in order to provide a timelywarning to the user/agent. For example, the reinforcement learning modelframework 28 may continually monitor, update, and compare the rewardfunction or risk score against a penalty or warning threshold value todetermine when the minimum distance measure is violated for a high-riskentity (e.g., risk score >0.8). As disclosed herein, the minimumdistance measure and advance warning times to provide optimal feedbackto the user/agent may be varied for each of the entities E={E1, E2, . .. En} in order to reduce the risk score in an iterative fashion.

To enable personalized user feedback of the desired minimum proximitydistance for detected entities, the visual assist engine 26 may alsoinclude a feedback sensor (e.g., keypad or audio detector) to providepersonalized feedback information from the user feedback and/or userheuristics which specify minimum proximity distance measures fordifferent objects/entities and associated states or conditions, such aspeople (male or female individuals), animals (e.g., menacing or friendlydogs or cats), moving or stationary vehicles, etc. For instance, a givenuser may specify a minimum distance to keep from people owning pets oreven high-speed vehicles driving on the curb side (user's preferencesmay vary based on their nature/demographics), but may specify adifferent minimum distance where the user is more comfortable with otherphysical entities, like kids walking around the park/pavement. Inaddition, the minimum distances may be dynamically adjusted based on astate of the user (e.g., the user's comfort level) and/or the identifiedobject/entity (e.g., a minimum distance is increased for a growling orbarking dog).

As disclosed herein, the reinforcement learning model framework 28 maygenerate user navigation control actions to direct the user's temporallocation in relation to the detected objects by providing alerts to theuser/agent about the location of detected objects. For example, thereinforcement learning model framework 28 may initiate a trigger messagewith a payload (e.g., json: <auditory message notification, distance,object in space and directions to move, haptic feedback type>) that isprovided to a wearable device/glass 6, such as by conveying the triggermessage over the wireless communication link 6 to update thevisually-impaired user/agent to maintain x distance from the object inorder to maximize safety for the user/agent. In addition or in thealternative, the guidance assistance device 20 may include a hapticand/or auditory feedback generator, such as, for example, a vibrator oraudio speaker (not shown) to provide personalized navigation guidancefeedback when a specified minimum proximity distance measure for anobject/entity is approaching or met. In selected embodiments, theguidance assistance device 20 may provide haptic/auditory feedback tothe user to keep a distance from a certain object along line oftrail/sight when user-specified feedback specifies theadversity/repercussions with respect to an identified object. Forexample, the feedback information may be provided to a wearable devicewhich indicates to the user when an object or entity is within a minimumspecified distance to the user with feedback selected from a groupconsisting of audio, visual, and haptic based on user's profile.

As indicated with the dashed lines around the machine learning (ML)model 28 block, the model may be located in whole or in part in theguidance assistance device 20 and/or computing system 21, but mayalternatively be located in the server computing system 101 which isconnected to exchange data 31, 32 over the network 30 with the guidanceassistance device 20. In such embodiments, the server computing system101 may include one or more system pipelines 101A, 101B, each of whichincludes a knowledge manager computing device 104 (comprising one ormore processors and one or more memories, and potentially any othercomputing device elements generally known in the art including buses,storage devices, communication interfaces, and the like) for processinginformation data 31 received from the guidance assistance device 20, aswell as information data 103 received over the network 102 from one ormore users at computing devices (e.g., 110, 120, 130). Over thenetwork(s) 30, 102, the computing devices communicate with each otherand with other devices or components via one or more wired and/orwireless data communication links, where each communication link maycomprise one or more of wires, routers, switches, transmitters,receivers, or the like. In this networked arrangement, the computingsystems 21, 101 and networks 30, 102 may be used with components,systems, sub-systems, and/or devices other than those that are depictedherein.

In the server computing system 101, the knowledge manager 104 may beconfigured with an information handling system 105 to receive inputsfrom various sources. For example, knowledge manager 104 may receiveinput from the network 102, one or more knowledge bases or corpora 106of electronic documents 107, semantic data 108, or other data, contentusers, and other possible sources of input. In selected embodiments, theknowledge base 106 may include structured, semi-structured, and/orunstructured content in a plurality of documents that are contained inone or more large knowledge databases or corpora. In addition, theserver computing system 101 may be connected to communicate withdifferent types of information handling systems which range from smallhandheld devices, such as handheld computer/mobile telephone 110 tolarge mainframe systems, such as mainframe computer 170. Examples ofhandheld computer 110 include personal digital assistants (PDAs),personal entertainment devices, such as MP3 players, portabletelevisions, and compact disc players. Other examples of informationhandling systems include pen or tablet computer 120, laptop or notebookcomputer 130, personal computer system 150, and server 160. As shown,the various information handling systems can be networked together usingcomputer network 102. Types of computer network 102 that can be used tointerconnect the various information handling systems include Local AreaNetworks (LANs), Wireless Local Area Networks (WLANs), the Internet, thePublic Switched Telephone Network (PSTN), other wireless networks, andany other network topology that can be used to interconnect theinformation handling systems. Many of the information handling systemsinclude nonvolatile data stores, such as hard drives and/or nonvolatilememory. Some of the information handling systems may use separatenonvolatile data stores (e.g., server 160 utilizes nonvolatile datastore 165, and mainframe computer 170 utilizes nonvolatile data store175). The nonvolatile data store can be a component that is external tothe various information handling systems or can be internal to one ofthe information handling systems. An illustrative example of aninformation handling system showing an exemplary processor and variouscomponents commonly accessed by the processor is shown in FIG. 3 .

To provide additional details for an improved understanding of selectedembodiments of the present disclosure, reference is now made to FIG. 3which illustrates an information handling system 200, more particularly,a processor and common components, which is a simplified example of acomputer system capable of performing the computing operations describedherein. Information handling system 200 includes one or more processors210 coupled to processor interface bus 212. Processor interface bus 212connects processors 210 to Northbridge 215, which is also known as theMemory Controller Hub (MCH). Northbridge 215 connects to system memory220 and provides a means for processor(s) 210 to access the systemmemory. In the system memory 220, a variety of programs may be stored inone or more memory devices, including a visual assist engine module 221which may be invoked for processing video/image data of an environmentto dynamically identify and classify different objects or entities Eiinto associated categorizations Ci (C1, C2, . . . , Cn) and a detecteddistances Di (D1, D2, . . . , Dn) for purposes of evaluation againstuser-specific distance preferences for each category in order to provideuser navigation control actions to help with user navigation through anenvironment. In addition or in the alternative, the system memory 220may include an artificial intelligence (AI) machine learning (ML) model222, such as a reinforcement learning model, which is trained to providefeedback information to the user about the location or position ofselected objects or entities in the environment. Graphics controller 225also connects to Northbridge 215. In one embodiment, PCI Express bus 218connects Northbridge 215 to graphics controller 225. Graphics controller225 connects to display device 230, such as a computer monitor.

Northbridge 215 and Southbridge 235 connect to each other using bus 219.In one embodiment, the bus is a Direct Media Interface (DMI) bus thattransfers data at high speeds in each direction between Northbridge 215and Southbridge 235. In another embodiment, a Peripheral ComponentInterconnect (PCI) bus connects the Northbridge and the Southbridge.Southbridge 235, also known as the I/O Controller Hub (ICH) is a chipthat generally implements capabilities that operate at slower speedsthan the capabilities provided by the Northbridge. Southbridge 235typically provides various busses used to connect various components.These busses include, for example, PCI and PCI Express busses, an ISAbus, a System Management Bus (SMBus or SMB), and/or a Low Pin Count(LPC) bus. The LPC bus often connects low-bandwidth devices, such asboot ROM 296 and “legacy” I/O devices (using a “super I/O” chip). The“legacy” I/O devices (298) can include, for example, serial and parallelports, keyboard, mouse, and/or a floppy disk controller. Othercomponents often included in Southbridge 235 include a Direct MemoryAccess (DMA) controller, a Programmable Interrupt Controller (PIC), anda storage device controller, which connects Southbridge 235 tononvolatile storage device 285, such as a hard disk drive, using bus284.

ExpressCard 255 is a slot that connects hot-pluggable devices to theinformation handling system. ExpressCard 255 supports both PCI Expressand USB connectivity as it connects to Southbridge 235 using both theUniversal Serial Bus (USB) the PCI Express bus. Southbridge 235 includesUSB Controller 240 that provides USB connectivity to devices thatconnect to the USB. These devices include webcam (camera) 250, infrared(IR) receiver 248, keyboard and trackpad 244, and Bluetooth device 246,which provides for wireless personal area networks (PANs). USBController 240 also provides USB connectivity to other miscellaneous USBconnected devices 242, such as a mouse, removable nonvolatile storagedevice 245, modems, network cards, ISDN connectors, fax, printers, USBhubs, and many other types of USB connected devices. While removablenonvolatile storage device 245 is shown as a USB-connected device,removable nonvolatile storage device 245 could be connected using adifferent interface, such as a Firewire interface, etc.

Wireless Local Area Network (LAN) device 275 connects to Southbridge 235via the PCI or PCI Express bus 272. LAN device 275 typically implementsone of the IEEE 802.11 standards for over-the-air modulation techniquesto wireless communicate between information handling system 200 andanother computer system or device. Extensible Firmware Interface (EFI)manager 280 connects to Southbridge 235 via Serial Peripheral Interface(SPI) bus 278 and is used to interface between an operating system andplatform firmware. Optical storage device 290 connects to Southbridge235 using Serial ATA (SATA) bus 288. Serial ATA adapters and devicescommunicate over a high-speed serial link. The Serial ATA bus alsoconnects Southbridge 235 to other forms of storage devices, such as harddisk drives. Audio circuitry 260, such as a sound card, connects toSouthbridge 235 via bus 258. Audio circuitry 260 also providesfunctionality such as audio line-in and optical digital audio in port262, optical digital output and headphone jack 264, internal speakers266, and internal microphone 268. Ethernet controller 270 connects toSouthbridge 235 using a bus, such as the PCI or PCI Express bus.Ethernet controller 270 connects information handling system 200 to acomputer network, such as a Local Area Network (LAN), the Internet, andother public and private computer networks.

While FIG. 3 shows one information handling system, an informationhandling system may take many forms, some of which are shown in FIG. 2 .For example, an information handling system may take the form of adesktop, server, portable, laptop, notebook, or other form factorcomputer or data processing system. In addition, an information handlingsystem may take other form factors such as a personal digital assistant(PDA), a gaming device, ATM machine, a portable telephone device, acommunication device or other devices that include a processor andmemory. In addition, an information handling system need not necessarilyembody the north bridge/south bridge controller architecture, as it willbe appreciated that other architectures may also be employed.

To provide additional details for an improved understanding of selectedembodiments of the present disclosure, there is provided hereinbelow apseudocode description of the operating algorithm for training anddeploying a reinforcement learning in an augmented reality (AR) edgedevice which iteratively categorizes and computes proximal distancemeasures for various object types in a vicinity of a person. In thisexample, the video content is captured in a time sequence Ti withdifferent frames being processed at each timer interval (e.g., T1, T2, .. . Tn→F={F1, F2, . . . Fn}) to identify one or more Entities E→E1 . . .Ej in each frame Fm at time Tm which are classified (e.g., with the CNNclassifier module 27A) and clustered (e.g., with the KNN cluster module27B) into categories:

-   -   ///CNN engine classifying the entities identified at time Tm in        a frame Fm.        -   # Initializing the CNN        -   classifier=Sequential( )        -   # Compiling the CNN        -   classifier.compile(optimizer=‘adam’, loss=‘categorical            crossentropy’, metrics=[‘accuracy’])        -   KNN to cluster the given entity in a categorical feature set            to generate: Clusters: {C1, C2 . . . Cn} created    -   ///computing distance to the entity from the smart glass:        -   def distance_to_glass(knownWidth, focalLength, perWidth):        -   # compute and return the distance from the entity to the            glass        -   return d=(knownWidth*focalLength)/perWidth        -   //D from a particular entity cluster identified i.e. Cm is            fed to RL model as part of observations, i.e. current agent            position p, distance d, from entity/entity cluster E′/Cn′    -   //User heuristics are proportional to safe distances predefined        (gathered from given user's edge device-mobile device) or        computed by assessing agent's reactions: Emotion API for        classifying the danger/risk level pertaining to a given        entity/cluster→E/C.    -   //RL model to ingest risk levels based on object classification        and updated risk score to optimally trigger a haptic feedback        and curate the distance from the entity/entities (normalization        action) for a personalized user by running said mechanism on the        edge device (smart glass with wearable).    -   Q value updated based on risk parameters and distance to an        identified E/C in a given timeframe t.    -   User actions→batch_action <action sequence>: <angle A, direction        of movement M (L, R, T, B)>    -   batch_state: Entities in a given state Em-En    -   •    -   class Dqn( ):        -   def learn(self, batch_state, batch_next_state, batch_reward,            batch_action):            -   outputs=self model(batch_state).gather(1,                batch_action.unsqueeze(1)).squeeze(1)            -   next_outputs=self.model(batch_next_state).detach(                )max(1)[0]            -   target=self.gamma*next_outputs+batch_reward            -   td_loss=F.smooth_l1_loss(outputs, target)            -   self.optimizer.zero_grad( )            -   td_loss.backward(retain_variables=True)            -   self.optimizer.step( )    -   Agent New State and reward/action computed with distance d and        new action fed to memory def update(self, reward, new_signal):        -   new_state=torch.Tensor(new_signal).float( )unsqueeze(0)            -   self.memory.push((self.last_state, new_state,                torch.LongTensor([int(self.last_action)]),                torch.Tensor([self.last_reward])))        -   action=self.select_action(new_state)        -   if len(self.memory.memory)>100:            -   batch_state, batch_next_state, batch_action,                batch_reward=self.memory.sample(100)            -   self.learn(batch_state, batch_next_state, batch_reward,                batch_action)        -   self.last_action=action        -   self.last_state=new_state        -   self.last_reward=reward        -   return action //Action determined by the agent    -   def score(self): //Reward computed based on distance and risk        score        -   return sum(self.reward_window)/(len(self.reward_window)+1.)

Based on above training, a user feedback trigger command is initiatedwith a payload (e.g., auditory message notification about distance,object in space and directions to move, haptic feedback in json) on thewearable device/glass via sensor data from the glass and wearable sensordata to update the visually impaired individual to maintain x distancefor satisfaction maximization (objective).

To provide additional details for an improved understanding of selectedembodiments of the present disclosure, reference is now made to FIG. 4which depicts a simplified flow chart 400 showing the logic for aidingthe navigation of a user through an environment in accordance withselected embodiments of the present disclosure. The processing shown inFIG. 4 may be performed by a cognitive system, such as the firstcomputing system 21, server computing system 101, or other naturallanguage question answering system.

FIG. 4 processing commences at 401, such as when user activates a visualassist edge device and/or turns ON a pair of smart glasses with anembedded camera and assist processor. As will be appreciated, the smartglasses may be provided separately from or integrated with a mobilevisual assist edge device.

At step 402, a network connection is established between the visualinput device (e.g., camera) and the visual assist edge device. Forexample, an adhoc wireless or Bluetooth network may be establishedbetween the smart glasses, the wearable device (e.g., watch) and/or themobile visual assist edge device. As part of the established network, awireless communication link may be established with a remote servercomputer system which hosts a reinforcement learning model incombination with synchronized communications with other edge devices toprovide assistive feedback to the user and/or other mobile visual assistedge devices. As will be appreciated, any suitable network connectionmay be used to connect to the remote server computer system, including,but not limited to Intranet, Internet, Wireless communication network,Wired communication network, Satellite communication network, etc.

At step 403, the surrounding state space or environment is monitored forobjects or entities over time by processing multiple video frames fromthe captured video data. In selected embodiments, the monitoring processmay be performed by using the smart glasses to capture video data of thesurrounding environment for monitoring the objects, though othertechniques may be used.

At step 404, the objects in the surrounding environment are identifiedand/or classified and associated distance measure are determined fromthe captured video data. In selected embodiments, a first computingsystem (e.g., computing system 21) may process captured video data fromthe smart glasses with a visual assist engine to classify or identifyobjects or entities in the surrounding environment which are identifiedin the captured video data. For example, an R-CNN classificationalgorithm may be executed on the smart glass with the assist processorto identify the entities (e.g., kids, dogs, cats, person (male/female),vehicle, vehicle type) in the state space while the agent is navigatingthrough a given environment. In this way, state space monitoring isinitiated as part of reinforcement learning model using R-CNN. Inaddition, the first computing system (e.g., computing system 21) mayprocess captured video data to calculate a distance measure to eachidentified object using known parameters (e.g., knownWidth, focalLength,perWidth) to compute a return distance from the entity to the glass orto otherwise determine a current user/agent position p and distance dfrom the object/entity. However, it will be appreciated that anysuitable distance detection technique (e.g., radar) may be used todetermine the distances between proximate objects and the user.

At step 405, the identified objects or entities in the surroundingenvironment are grouped into clusters or categories based on the videoframe type and attributes. In selected embodiments, a first computingsystem (e.g., computing system 21) may process the identified objects orentities using vector processing techniques to group or cluster vectorrepresentations of each identified entity. For example, a K-nearestneighbor (KNN) algorithm may be executed in conjunction with CNN tocluster the entities into specific categories (e.g., clustering kids ina certain age range, clustering persons by gender, clustering vehiclesby size and/or speed, etc.). In other embodiments, a k-means clusteringalgorithm may be executed to categorize other objects based onsimilarity in attributes to classified objects.

At step 406, machine learning, natural language processing (NLP), and/orartificial intelligence (AI) processing techniques are applied, alone orin combination, to automatically determine individual minimum distancespacing preferences for each object or entity cluster or category basedon heuristics and/or user preferences pertaining to each object/entitycluster/category. In step 406, process any audio data and associate withentities based on audio classification and utilize natural languageprocessing when audio is speech and converted to text. For example, abarking sound will be identified as a bark, and associated with thattype of entity in the screen, which then changes the state of entity, inthis case a dog; a modification of an entity E1, which is a dog, wouldbe a “barking dog” or “angry dog” and not just a dog to represent E1.The natural language audio may be associated with an individual or theenvironment for use in analysis. A modification of an entity E1, whichis a dog, would be a “barking dog” or “angry dog” and not just a dog torepresent E1. In selected embodiments, a server computing system (e.g.,computing system 101) and/or first computing system 21 may employartificial intelligence processing techniques, such as a machinelearning model or reinforcement learning model, which are applied to theidentified entities E (E1, E2, . . . En) identified at step 404 and/orcategorization C (C1, C2, . . . Cn) generated at step 405 to compute,for each entity, a corresponding minimum distance spacing Dmin (Dmin1,Dmin2, . . . Dminn) from the entity Ei based on the categorization Cipreferred by a user. In selected embodiment, the minimum distancespacing Dmini is adjusted based on a state of the entity Ei so that, forexample, an “angry” dog has a larger minimum distance spacing than a“happy” dog. In addition or in the alternative, the minimum distancespacing Dmini may be iteratively monitored and adjusted based onlearning or observing heuristics and/or the user's comfort level forco-existing in proximity with a detected entity Ei.

In accordance with selected embodiments of the present disclosure, areinforcement learning (RL) module may be implemented at step 406 whichtakes into account four parameters: State S, Agent A, Reward R, andenvironment E. In this case, the environment parameter E specifies anenvironment where the user is navigating. In addition, the stateparameter S specifies the state of the user at a particular step in timeT with a group of entities in space. The agent parameter A specifies asoftware agent running in the cloud/edge device/AR device which iscapable of taking an action A′ for helping the user transition fromState S1 to S2, and may be quantified with a transition probabilitymeasure PT→S1 to S2 acted upon by agent A in a given environment E. Thereward parameter R specifies a reward or penalty function which isupdated or adjusted based on user feedback. For example, a rewardfunction (RF) can be incremented by an increment value x (e.g., RF=RF+x)which represents a positive step that is taken to assist the user and apositive feedback is exhibited/received by the user. In addition or inthe alternative, a penalty function (PF) can be decremented by andecrement value y (e.g., PF=PF−y) where y can be a value assignedproportional based on the level of dissatisfaction or incorrect guidanceprovided. In selected embodiments of the reinforcement learning module,a Deep Q-Network (DQN) algorithm may be applied which uses a Q table totake into account how the user is transitioning from one state toanother. As demonstrated with the DQN algorithm example below, thetarget Q function (in the rectangle block) basically keeps track ofreward function, state transition parameters, etc. to keep track of howto maximize the reward function over multiple iterations and achieve aglobal maxima:

$ {Q( {s_{t},a} )}arrow{{Q( {s_{t},a} )} + {{\alpha\lbrack {r_{t + 1} + {\gamma\;{\max\limits_{p}\;{Q( {s_{t + 1},p} )}}} - {Q( {s_{t},a} )}} \rbrack}.}} $

Over multiple training periods or iterations/epochs, the RL module isable to adapt to the user's persona and help the user navigate the spacewith other entities in a given environment with maximum reward functionvalue.

At step 407, feedback is provided to the user when an identified objector entity Ei is within (or approaching) the individual minimum distancespacing Dmini for that object/entity. In selected embodiments, thefeedback is provided to the user at a wearable device or AR edge devicehaving a haptic/audio/visual interface for generating an optimalnotification in the form of audio, visual, and haptic (e.g., vibratory)information based on the user's profile and/or environment. In selectedembodiments, a server computing system (e.g., computing system 101) mayemploy artificial intelligence processing techniques, such as a machinelearning model or reinforcement learning model, which are applied to theidentified entities E (E1, E2, . . . En) identified at step 404 and/orcategorization C (C1, C2, . . . Cn) generated at step 405 to compute,for each entity, a corresponding minimum distance spacing Dmin (Dmin1,Dmin2, . . . Dminn) from the entity Ei based on the categorization Cipreferred by a user. The feedback may be staged or increased in urgencyas the user approaches the individual minimum distance spacing Dmini foran object/entity Ei, such as by providing a first haptic feedback signalwhen the user is approaching the individual minimum distance spacingDmini, and providing a second, more urgent haptic feedback signal whenthe user reaches the individual minimum distance spacing Dmini.

In addition to providing the user feedback at step 407, the process isiteratively repeated to continue training the artificial intelligencemodel (step 406) to eventually configure the distance and notificationmetrics. In selected embodiments, the feedback mechanism may alsoinclude using an interface with distances assigned to different objectsso that the user can manually indicate the minimum distance preferencesfor each object. This iterative feedback process continues until theprocess ends at step 408.

By now, it will be appreciated that there is disclosed herein a system,method, apparatus, and computer program product for providing navigationguidance around various object types in a vicinity of a guidanceassistance device for a user. As disclosed, a first information handlingsystem comprising a processor and a memory receives data regarding anenvironment around the guidance assistance device. In selectedembodiments, the data regarding the environment is received at one ormore wearable devices worn by the user. In other embodiments, the dataregarding the environment is received by using a camera embedded in awearable device worn by the user to record video data which comprises ofaudio and video regarding the environment around the guidance assistancedevice. At the first information handling system, the data regarding theenvironment around the guidance assistance device is analyzed toidentify one or more entities E (E1, E2, . . . Ei). In selectedembodiments, the data regarding the environment around the guidanceassistance device is analyzed by employing a Region-Based ConvolutionalNeural Networks (R-CNN) model to identify from the video data one ormore entities E (E1, E2, . . . Ei) in space and audio to associate withentities in the space while the user is navigating through theenvironment. In addition, an artificial intelligence (AI) machinelearning analysis is applied to the identified entities E (E1, E2, . . .Ei) to group the one or more entities E (E1, E2, . . . Ei) intocorresponding categories C (C1, C2, . . . Cj) and determine a minimumspacing distance Dmin (D1, D2, . . . Di) for each of the one or moreentities E (E1, E2, . . . Ei), wherein the minimum spacing distance Dminis a minimum distance between the guidance assistance device and theentity Ei based on the categorization Ci specified by the user. Inselected embodiments, the minimum spacing distance Dmin for a firstentity may be adjusted based on a state of the first entity. In otherembodiments, the minimum spacing distance Dmin determined for each ofthe one or more entities E (E1, E2, . . . Ei) is iteratively monitoredand adjusted based on heuristics or a learned comfort level for the userbeing in proximity to the one or more entities E (E1, E2, . . . Ei). Inselected embodiments, the artificial intelligence (AI) machine learninganalysis is applied by deploying a reinforcement learning model on theguidance assistance device for the user to generate an optimalnotification to assist the user in navigating through the environment.The first information handling system provides feedback to the user whenany of the one or more entities E (E1, E2, . . . Ei) is within thanminimum spacing distance Dmin corresponding to said entity. In selectedembodiments, feedback provided to the user is selected from a groupconsisting of audio, visual, and haptic based on a profile for the userand state of a first entity.

While particular embodiments of the present invention have been shownand described, it will be obvious to those skilled in the art that,based upon the teachings herein, changes and modifications may be madewithout departing from this invention and its broader aspects.Therefore, the appended claims are to encompass within their scope allsuch changes and modifications as are within the true spirit and scopeof this invention. Furthermore, it is to be understood that theinvention is solely defined by the appended claims. It will beunderstood by those with skill in the art that if a specific number ofan introduced claim element is intended, such intent will be explicitlyrecited in the claim, and in the absence of such recitation no suchlimitation is present. For non-limiting example, as an aid tounderstanding, the following appended claims contain usage of theintroductory phrases “at least one” and “one or more” to introduce claimelements. However, the use of such phrases should not be construed toimply that the introduction of a claim element by the indefinitearticles “a” or “an” limits any particular claim containing suchintroduced claim element to inventions containing only one such element,even when the same claim includes the introductory phrases “one or more”or “at least one” and indefinite articles such as “a” or “an”; the sameholds true for the use in the claims of definite articles.

What is claimed is:
 1. A computer-implemented method for navigationguidance around various object types in a vicinity of a guidanceassistance device for a user, the method comprising: receiving, by afirst information handling system comprising a processor and a memory,data regarding an environment around the guidance assistance device;analyzing, by the first information handling system, the data regardingthe environment around the guidance assistance device to identify one ormore entities E (E1, E2, . . . Ei) and corresponding detected distancesD (D1, D2, . . . Di) between the guidance assistance device and the oneor more entities E (E1, E2, . . . Ei); applying an artificialintelligence (AI) machine learning analysis to group the one or moreentities E (E1, E2, . . . Ei) into corresponding categories C (C1, C2, .. . Cj) and to determine a minimum spacing distance Dmin (Dmin1, Dmin2,. . . Dmini) for each of the one or more entities E (E1, E2, . . . Ei)based on a personal space profile specifying user-specific distancepreferences for how close the user prefers to be in spatial relation toeach of the corresponding categories C (C1, C2, . . . Cj); and providingfeedback, by the first information handling system, to the user when anyof the detected distances D (D1, D2, . . . Di) corresponding to the oneor more entities E (E1, E2, . . . Ei) is within the than minimum spacingdistance Dmin (Dmin1, Dmin2, . . . Dmini) corresponding to said one ormore entities E (E1, E2, . . . Ei) entity.
 2. The computer-implementedmethod of claim 1, where receiving data regarding the environmentcomprises receiving data regarding the environment at one or morewearable devices worn by the user.
 3. The computer-implemented method ofclaim 1, where providing feedback to the user comprises providingfeedback selected from a group consisting of audio, visual, and hapticbased on a profile for the user and state of a first entity.
 4. Thecomputer-implemented method of claim 1, where the minimum spacingdistance Dmin for a first entity is adjusted based on a state of thefirst entity.
 5. The computer-implemented method of claim 1, wherein theminimum spacing distance Dmin determined for each of the one or moreentities E (E1, E2, . . . Ei) is iteratively monitored and adjustedbased on heuristics or a learned comfort level for the user being inproximity to the one or more entities E (E1, E2, . . . Ei).
 6. Thecomputer-implemented method of claim 1, where applying the artificialintelligence (AI) machine learning analysis comprises deploying areinforcement learning model on the guidance assistance device togenerate an optimal notification to assist the user in navigatingthrough the environment.
 7. The computer-implemented method of claim 1,where receiving data regarding the environment comprises using a cameraembedded in a wearable device worn by the user to record video and audiodata regarding the environment around the guidance assistance device. 8.The computer-implemented method of claim 7, where analyzing the dataregarding the environment around the guidance assistance devicecomprises employing a Region-Based Convolutional Neural Networks (R-CNN)model to identify from the video data, the one or more entities E (E1,E2, . . . Ei) in space and audio to associate with entities in the spacewhile the user is navigating through the environment.
 9. An informationhandling system comprising: one or more processors; a memory coupled toat least one of the processors; a set of instructions stored in thememory and executed by at least one of the processors to providenavigation guidance around various object types in a vicinity of aguidance assistance device for a user, wherein the set of instructionsare executable to perform actions of: receiving, by the system, dataregarding an environment around the guidance assistance device;analyzing, by the system, the data regarding the environment around theguidance assistance device to identify one or more entities E (E1, E2, .. . Ei) and corresponding detected distances D (D1, D2, . . . Di)between the guidance assistance device and the one or more entities E(E1, E2, . . . Ei); applying an artificial intelligence (AI) machinelearning analysis to group the one or more entities E (E1, E2, . . . Ei)into corresponding categories C (C1, C2, . . . Cj) and to determine aminimum spacing distance Dmin (Dmin1, Dmin2, . . . Dmini) for each ofthe one or more entities E (E1, E2, . . . Ei) based on a personal spaceprofile specifying user-specific distance preferences for how close theuser prefers to be in spatial relation to each of the correspondingcategories C (C1, C2, . . . Cj); and providing feedback, by the system,to the user when any of the detected distances D (D1, D2, . . . Di)corresponding to the one or more entities E (E1, E2, . . . Ei) is withinthe than minimum spacing distance Dmin (Dmin1, Dmin2, . . . Dmini)corresponding to said one or more entities E (E1, E2, . . . Ei) entity.10. The information handling system of claim 9, wherein the set ofinstructions are executable to provide feedback to the user by providingfeedback selected from a group consisting of audio, visual, and hapticbased on a profile for the user and state of a first entity.
 11. Theinformation handling system of claim 9, wherein the set of instructionsare executable to adjust the minimum spacing distance Dmin for a firstentity based on a state of the first entity.
 12. The informationhandling system of claim 9, wherein the set of instructions areexecutable to iteratively monitor and adjust the minimum spacingdistance Dmin for each of the one or more entities E (E1, E2, . . . Ei)based on heuristics or a learned comfort level for the user being inproximity to the one or more entities E (E1, E2, . . . Ei).
 13. Theinformation handling system of claim 10, wherein the set of instructionsare executable to apply the artificial intelligence (AI) machinelearning analysis by deploying a reinforcement learning model on theguidance assistance device to generate an optimal notification to assistthe user in navigating through the environment.
 14. The informationhandling system of claim 10, wherein the set of instructions areexecutable to receive data regarding the environment by receiving videodata from a camera embedded in a wearable device worn by the user torecord video and audio data regarding the environment around theguidance assistance device.
 15. The information handling system of claim14, wherein the set of instructions are executable to analyze the dataregarding the environment around the guidance assistance device byemploying a Region-Based Convolutional Neural Networks (R-CNN) model toidentify, from the video data, the one or more entities E (E1, E2, . . .Ei) in space and audio to associate with entities in the space while theuser is navigating through the environment.
 16. A computer programproduct stored in a computer readable storage medium, comprisingcomputer instructions that, when executed by an information handlingsystem, causes the system to assist with navigation guidance aroundvarious object types in a vicinity of a guidance assistance device for auser by: receiving, by the system comprising a processor and a memory,video data from a camera embedded in one or more wearable devices wornby the user to record video of the environment around the guidanceassistance device; analyzing, by the system, the video data regardingthe environment around the guidance assistance device to identify one ormore entities E (E1, E2, . . . Ei) and corresponding detected distancesD (D1, D2, . . . Di) between the guidance assistance device and the oneor more entities E (E1, E2, . . . Ei); applying an artificialintelligence (AI) machine learning analysis to group the one or moreentities E (E1, E2, . . . Ei) into corresponding categories C (C1, C2, .. . Cj) and to determine a minimum spacing distance Dmin (Dmin1, Dmin2,. . . Dmini) for each of the one or more entities E (E1, E2, . . . Ei)based on a personal space profile specifying user-specific distancepreferences for how close the user prefers to be in spatial relation toeach of the corresponding categories C (C1, C2, . . . Cj); and providingfeedback, by the system, to the user when any of the detected distancesD (D1, D2, . . . Di) corresponding to the one or more entities E (E1,E2, . . . Ei) is within the minimum spacing distance Dmin (Dmin1, Dmin2,. . . Dmini) corresponding to said one or more entities E (E1, E2, . . .Ei).
 17. The computer program product of claim 16, further comprisingcomputer instructions that, when executed by the system, causes thesystem to analyze the video data by employing a Region-BasedConvolutional Neural Networks (R-CNN) model to identify from the videodata one or more entities E (E1, E2, . . . Ei) in space and audio toassociate with entities in the space while the user is navigatingthrough the environment.
 18. The computer program product of claim 17,further comprising computer instructions that, when executed by thesystem, causes the system to cluster each of the one or more entities E(E1, E2, . . . Ei) into one of the corresponding categories C (C1, C2, .. . Cj).
 19. The computer program product of claim 18, furthercomprising computer instructions that, when executed by the system,causes the system to apply the artificial intelligence (AI) machinelearning analysis by using a reinforcement learning model on theguidance assistance device to generate an optimal notification to assistthe user in navigating through the environment.
 20. The computer programproduct of claim 19, further comprising computer instructions that, whenexecuted by the system, causes the system to iteratively monitor andadjust the minimum spacing distance Dmin for a first entity based on astate of the first entity or based on heuristics or a learned comfortlevel for the user being in proximity to the one or more entities E (E1,E2, . . . Ei).